visual_prep function

Batch Effect Diagnostic Visualization Preparation

Prepare relevant datasets and statistical test results for batch/site effect diagnostic visualization.

visual_prep(
  type = "lm",
  features,
  batch,
  covariates = NULL,
  interaction = NULL,
  random = NULL,
  smooth = NULL,
  smooth_int_type = NULL,
  df,
  cores = detectCores(),
  mdmr = TRUE
)

Arguments

• type: The name of a regression model to be used in batch effect diagnostics stage: "lmer", "lm", "gam".
• features: The name of the features to be evaluated.
• batch: The name of the batch variable.
• covariates: Name of covariates supplied to model.
• interaction: Expression of interaction terms supplied to model (eg: "age,diagnosis").
• random: Variable name of a random effect in linear mixed effect model.
• smooth: Variable name that requires a smooth function.
• smooth_int_type: Indicates the type of interaction in gam models. By default, smooth_int_type is set to be "linear", representing linear interaction terms. "categorical-continuous", "factor-smooth" both represent categorical-continuous interactions ("factor-smooth" includes categorical variable as part of the smooth), "tensor" represents interactions with different scales, and "smooth-smooth" represents interaction between smoothed variables.
• df: Dataset to be evaluated.
• cores: number of cores used for parallel computing.
• mdmr: A boolean variable indicating whether to run the MDMR test (default: TRUE).

Returns

visual_prep returns a list containing the following components: - residual_add_df: Residuals that might contain additive and multiplicative joint batch effects

• residual_ml_df: Residuals that might contain multiplicative batch effect

• pr.feature: PCA results

• pca_summary: A dataframe containing the variance explained by Principal Components (PCs)

• pca_df: A dataframe contains features in the form of PCs

• tsne_df: A dataframe prepared for T-SNE plots

• kr_test_df: A dataframe contains Kenward-Roger(KR) test results

• fk_test_df: A dataframe contains Fligner-Killeen(FK) test results

• mdmr.summary: A dataframe contains MDMR results

• anova_test_df: A dataframe contains ANOVA test results

• kw_test_df: A dataframe contains Kruskal-Wallis test results

• lv_test_df: A dataframe contains Levene's test results

• bl_test_df: A dataframe contains Bartlett's test results

• red: A parameter to highlight significant p-values in result table

• info: A list contains input information like batch, covariates, df etc

Examples

visual_prep(type = "lm", features = colnames(adni)[43:53], batch = "manufac",
covariates = c("AGE", "SEX", "DIAGNOSIS"), df = head(adni, 500), cores = 1)